# Western Ugandan Crater Lakes 1000 Year Diatom-inferred Lake Level and Conductivity #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # NOTE: Please cite original publication, online resource and date accessed when using this data. # If there is no publication information, please cite Investigator, title, online resource and date accessed. # # Description/Documentation lines begin with # # Data lines have no # # # Online_Resource: http://ncdc.noaa.gov/paleo/study/16791 # # Original_Source_URL: ftp://ftp.ncdc.noaa.gov/pub/data/paleo/paleolimnology/eastafrica/nyamogusingiri2014recon.txt # # Archive: Paleolimnology #--------------------------------------- # Contribution_Date # Date: 2014-06-26 #--------------------------------------- # Title # Study_Name: Western Ugandan Crater Lakes 1000 Year Diatom-inferred Lake Level and Conductivity #--------------------------------------- # Investigators # Investigators: Mills, K.; Ryves, D.B. #--------------------------------------- # Description and Notes # Description: Diatom census data and reconstructed Lake Level and Salinity for two East African lakes over the past 1000 years. # #--------------------------------------- # Publication # Authors: Mills, K.; Ryves, D.B.; Anderson, N.J.; Bryant, C.L. and Tyler, J.J. # Published_Date_or_Year: 2013 # Published_Title: Expressions of climate perturbations in western Ugandan crater lake sediment records during the last 1000 yr # Journal_Name: Climate of the Past Discussions # Volume: 9 # Issue: 5 # Pages: 5183-5226 # Report Number: # DOI: 10.5194/cpd-9-5183-2013 # Online_Resource: http://www.clim-past-discuss.net/9/5183/2013/cpd-9-5183-2013.html # Abstract: Equatorial East Africa has a complex, regional patchwork of climate regimes, with multiple interacting drivers. Recent studies have focussed on large lakes and reveal signals that are smoothed in both space and time, and, whilst useful at a continental scale, are of less relevance when understanding short-term, abrupt or immediate impacts of climate and environmental changes. Smaller-scale studies have highlighted spatial complexity and regional heterogeneity of tropical palaeoenvironments in terms of responses to climatic forcing (e.g. the Little Ice Age [LIA]) and questions remain over the spatial extent and synchroneity of climatic changes seen in East African records. Sediment cores from paired crater lakes in western Uganda were examined to assess ecosystem response to long-term climate and environmental change as well as testing responses to multiple drivers using redundancy analysis. These archives provide annual to sub-decadal records of environmental change. The records from the two lakes demonstrate an individualistic response to external (e.g. climatic) drivers, however, some of the broader patterns observed across East Africa suggest that the lakes are indeed sensitive to climatic perturbations such as a dry Mediaeval Climate Anomaly (MCA; 1000-1200 AD) and a relatively drier climate during the main phase of the LIA (1500-1800 AD); though lake levels in western Uganda do fluctuate. The relationship of Ugandan lakes to regional climate drivers breaks down c. 1800 AD, when major changes in the ecosystems appear to be a response to sediment and nutrient influxes as a result of increasing cultural impacts within the lake catchments. The data highlight the complexity of individual lake response to climate forcing, indicating shifting drivers through time. This research also highlights the importance of using multi-lake studies within a landscape to allow for rigorous testing of climate reconstructions, forcing and ecosystem response. #--------------------------------------- # Publication # Authors: Mills K. and Ryves D.B. # Published_Date_or_Year: 2012 # Published_Title: Diatom-based models for inferring past water chemistry in western Ugandan crater lakes # Journal_Name: Journal of Paleolimnology # Volume: 48 # Issue: # Pages: 383-399 # Report Number: # DOI: 10.1007/s10933-012-9609-2 # Abstract: Diatom surface sediment samples and corresponding water chemistry were collected from 56 lakes across a natural conductivity gradient in western Uganda (reflecting a regional climatic gradient of effective moisture) to explore factors controlling diatom distribution. Here we develop a regional training set from these crater lakes to test the hypothesis that this approach, by providing more appropriate and closer analogues, can improve the accuracy of palaeo-conductivity reconstructions, and so environmental inferences in these lake systems compared to larger training sets. We compare this output to models based on larger, but geographically and limnologically diverse training sets, using the European Diatom Database Initiative (EDDI) database. The relationships between water chemistry and diatom distributions were explored using canonical correspondence analysis (CCA) and partial CCA. Variance partitioning indicated that conductivity accounted for a significant and independent portion of this variation. A transfer function was developed for conductivity (r jack 2 = 0.74). Prediction errors, estimated using jack-knifing, are low for the conductivity model (0.256 log10 units). The resulting model was applied to a sedimentary sequence from Lake Kasenda, western Uganda. Comparison of conductivity reconstructions using the Ugandan crater lake training set and the East Africa training set (EDDI) highlighted a number of differences in the optima of key diatom taxa, which lead to differences in reconstructed values and could lead to misinterpretation of the fossil record. This study highlights issues of how far transfer functions based on continental-scale lake datasets such as the EDDI pan-African models should be used and the benefits that may be obtained from regional training sets. #--------------------------------------- # Funding_Agency # Funding_Agency_Name: Natural Environment Research Council (NERC) # Grant: NE/D0001557/1 #--------------------------------------- # Funding_Agency # Funding_Agency_Name: Loughborough University Development Fund # Grant: #--------------------------------------- # Site Information # Site_Name: Lake Nyamogusingiri # Location: Africa>Eastern Africa>Uganda # Country: Uganda # Northernmost_Latitude: -0.284583 # Southernmost_Latitude: -0.284583 # Easternmost_Longitude: 30.012972 # Westernmost_Longitude: 30.012972 # Elevation: 984 m #--------------------------------------- # Data_Collection # Collection_Name: Nyamogusingiri2014recon # First_Year: 1144 # Last_Year: 2007 # Time_Unit: AD # Core_Length: 127 # Notes: The data provided are from a composite core sequence. This master core sequence consists of a number of correlated, overlapping cores. Additional details are provided in the associated publication (Mills et al., 2014). #--------------------------------------- # Chronology: # Lake Nyamogusingiri age data. Published age model was created in CLAM for R, following the method of Blaauw (2010). # Additional chronology based on Pb-210 and Cs-137 data is also provided. # # # Labcode depth_top depth_bottom mat.dated 14C.raw 14C.raw_err datemeth calib.14C calib.14C_2sig_lo calib.14C_2sig_up calib_method rejected # SUERC-18911 61 62 Leaf / Charcoal 419 37 14C AMS 1467 1422 1625 IntCal09 No # SUERC-19066 92 93 Leaf / Charcoal 685 35 14C AMS 1299 1265 1390 IntCal09 No # SUERC-19067 108 109 Wood /Charcoal 795 35 14C AMS 1239 1180 1278 IntCal09 No # SUERC-18396 121 122 Wood 494 37 14C AMS 1426 1326 1454 IntCal09 Yes # POZ-26361 126 127 Charcoal 415 30 14C AMS 1464 1429 1618 IntCal09 Yes # # # code mid_depth mat.dated age_AD year error datemeth # Nyam-1 0 Bulk sediment 2007 0 0 Pb-210, corrected using Cs-137 # Nyam-2 0.5 Bulk sediment 2006 1 1 Pb-210, corrected using Cs-137 # Nyam-3 2.5 Bulk sediment 2003 4 1 Pb-210, corrected using Cs-137 # Nyam-4 4.5 Bulk sediment 2000 7 2 Pb-210, corrected using Cs-137 # Nyam-5 8.5 Bulk sediment 1994 13 2 Pb-210, corrected using Cs-137 # Nyam-6 10.5 Bulk sediment 1992 15 2 Pb-210, corrected using Cs-137 # Nyam-7 12.5 Bulk sediment 1989 18 2 Pb-210, corrected using Cs-137 # Nyam-8 14.5 Bulk sediment 1987 20 3 Pb-210, corrected using Cs-137 # Nyam-9 16.5 Bulk sediment 1984 23 3 Pb-210, corrected using Cs-137 # Nyam-10 18.5 Bulk sediment 1980 27 4 Pb-210, corrected using Cs-137 # Nyam-11 21.5 Bulk sediment 1972 35 6 Pb-210, corrected using Cs-137 # Nyam-12 24.5 Bulk sediment 1963 44 8 Pb-210, corrected using Cs-137 # Nyam-13 27.5 Bulk sediment 1952 55 9 Pb-210, corrected using Cs-137 # Nyam-14 30.5 Bulk sediment 1944 63 9 Pb-210, corrected using Cs-137 # Nyam-15 33.5 Bulk sediment 1939 68 9 Pb-210, corrected using Cs-137 # Nyam-16 35.5 Bulk sediment 1934 73 9 Pb-210, corrected using Cs-137 # Nyam-17 37.5 Bulk sediment 1928 79 9 Pb-210, corrected using Cs-137 # Nyam-18 39.5 Bulk sediment 1920 87 9 Pb-210, corrected using Cs-137 # Nyam-19 41.5 Bulk sediment 1910 97 10 Pb-210, corrected using Cs-137 # Nyam-20 43.5 Bulk sediment 1900 106.9 11 Pb-210, corrected using Cs-137 # Nyam-21 45.5 Bulk sediment 1890 116.7 12 Pb-210, corrected using Cs-137 # Nyam-22 47.5 Bulk sediment 1880 126.7 14 Pb-210, corrected using Cs-137 # Nyam-23 49.5 Bulk sediment 1869 137.9 17 Pb-210, corrected using Cs-137 # Nyam-24 51.5 Bulk sediment 1858 149.3 18 Pb-210, corrected using Cs-137 # # # #--------------------------------------- # Variables # # Data variables follow that are preceded by "##" in columns one and two. # Data line variables format: Variables list, one per line, shortname-tab-longname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) # ## depth_cm Depth,,,cm,,,,,N ## age_AD age,,,year AD,,,,analytical technique: isotope ratio mass spectrometry (AMS),N ## DI_level Diatom-inferred lake level,,,N/A,,Paleolimnology,,Percentage planktonic data. Loess smooth used on published data.,N ## dca_1 Detrended Correspondence Analysis axis 1 sample scores,,,SI Units,,Paleolimnology,,Ordination analysis: Canoco 4.5,N ## dca_2 Detrended Correspondence Analysis axis 2 sample scores,,,SI Units,,Paleolimnology,,Ordination analysis: Canoco 4.5,N ## DI_cond Diatom-Inferred conductivity,,,µS/cm,,Paleolimnology,,Diatom conductivity transfer function,N ## error_min Diatom-Inferred conductivity (error - min),,RMSEP (Jack-knifed),µS/cm,,Paleolimnology,,Diatom conductivity transfer function,N ## error_max Diatom-Inferred conductivity (error - max),,RMSEP (Jack-knifed),µS/cm,,Paleolimnology,Model error of 0.256 log units,Diatom conductivity transfer function,N #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: # depth_cm age_AD DI_level dca_1 dca_2 DI_cond error_min error_max 0 2007 22.83 1.25 0.7952 439.886 243.972 793.122 0.5 2006 23.96 1.45 0.9171 461.392 255.9 831.898 1 2005 17.70 1.33 0.866 529.883 293.887 955.388 2 2004 22.66 1.35 0.7674 537.353 298.03 968.857 3 2002 18.86 1.43 0.9004 592.407 328.564 1068.12 4 2001 20.00 1.26 0.6687 481.127 266.845 867.481 5 1999 19.81 1.24 0.5174 519.661 288.217 936.958 6 1998 17.39 0.87 0.2706 466.81 258.905 841.667 7 1996 13.73 0.83 0.3043 435.291 241.424 784.837 8 1995 21.00 0.60 0.3802 556.391 308.589 1003.18 9 1994 21.50 0.38 0.0478 371.185 205.869 669.253 10 1993 18.45 0.43 0.2084 396.479 219.897 714.859 11 1991 25.8462 0.12 0.1589 405.042 224.647 730.298 12 1990 26.6458 0.3468 0 407.315 225.907 734.396 13 1989 22.2892 0.137 0.0546 476.738 264.411 859.567 14 1987 25.9475 0 0.1308 410.885 227.887 740.833 15 1986 41.5473 0.2211 0.3493 549.819 304.944 991.333 16 1984 18.6969 0.0148 0.2447 421.706 233.889 760.343 17 1983 18.2058 0.1534 0.3189 489.689 271.594 882.918 18 1980 9.58084 0.5576 0.3597 641.254 355.656 1156.19 19 1978 0.593472 0.7937 1.0671 993.688 551.125 1791.64 20 1976 2.32558 0.9437 1.1992 879.711 487.91 1586.13 21 1973 3.1339 0.7641 1.1933 1184.54 656.976 2135.75 22 1970 1.43678 0.7965 1.1191 1043.09 578.525 1880.71 23 1967 4.1543 0.9206 1.1427 1049.11 581.863 1891.56 24 1964 5.32915 0.9386 1.2234 1203.43 667.453 2169.81 25 1961 11.3514 0.6742 1.3017 1456.16 807.624 2625.48 26 1958 11.4206 0.7612 1.3722 1027.33 569.784 1852.29 27 1955 20.2492 0.5489 1.3164 797.462 442.293 1437.84 28 1953 10.7266 0.5728 1.7604 1065.96 591.209 1921.94 29 1950 8.79121 0.5224 1.942 1656.99 919.009 2987.58 30 1947 26.2431 0.534 2.0341 2255.23 1250.81 4066.22 31 1945 11.8182 0.4297 1.7428 1230.24 682.323 2218.14 32 1942 27.9539 0.6687 1.9146 1455.12 807.047 2623.61 33 1940 13.1868 0.6984 1.7061 1708.87 947.783 3081.12 34 1937 36.2881 0.4731 1.7628 907.194 503.153 1635.69 35 1935 15.2778 0.5536 1.78 758.316 420.582 1367.26 35.5 1933.5 27.1523 0.3737 1.8684 998.114 553.58 1799.62 36 1932 9.58084 0.8254 1.7099 612.393 339.649 1104.16 37 1929 26.1845 0.9272 1.5403 630.057 349.446 1136 38 1926 11.9632 0.5674 1.6392 660.861 366.531 1191.54 39 1923 36.6667 0.7628 1.3945 494.242 274.119 891.127 40 1919 9.51157 0.8203 1.5533 643.087 356.673 1159.5 41 1916 25 1.1251 1.2369 600.593 333.104 1082.88 42 1911 9.91254 1.3501 1.3023 555.418 308.049 1001.43 43 1907 24.6032 1.2143 1.46 470.251 260.813 847.871 44 1902 7.36196 1.048 1.518 537.316 298.009 968.79 45 1896 25.6997 1.139 1.1486 507.049 281.222 914.218 46 1889 14.3293 1.3262 1.4458 519.481 288.118 936.633 47 1882 7.93201 1.3449 1.3473 489.948 271.738 883.385 48 1872 15.0538 1.6544 1.2876 505.01 280.092 910.542 49 1861 10.5714 1.5652 1.4847 496.478 275.359 895.159 50 1849 9.79381 1.6185 1.2874 509.565 282.618 918.755 51 1834 10.1227 1.7035 1.3301 508.827 282.209 917.424 52 1817 9.55224 1.5837 1.3316 489.238 271.344 882.105 53 1798 18.5294 1.7143 1.3664 494.186 274.088 891.026 54 1778 30.5085 1.8053 1.2552 487.405 270.327 878.8 55 1757 22.1893 1.9224 0.952 514.411 285.306 927.492 56 1734 32.5373 1.7316 1.0146 506.058 280.673 912.432 57 1711 24.7678 1.7323 1.0064 513.405 284.748 925.678 58 1688 36.8098 1.7897 1.0259 498.483 276.471 898.774 59 1665 26.935 1.9264 0.8166 557.507 309.208 1005.2 60 1642 39.4886 1.924 0.8386 517.786 287.177 933.577 61 1620 54.4041 1.8311 0.9031 505.464 280.343 911.361 62 1598 39.2638 1.8529 0.8011 524.047 290.65 944.866 63 1578 44.5378 2.0331 0.9453 535.908 297.228 966.252 64 1559 54.9858 2.0614 0.7675 555.648 308.177 1001.84 65 1541 58.9971 1.9287 0.9903 561.255 311.286 1011.95 66 1524 59.8746 1.8005 0.781 543.951 301.689 980.753 67 1508 44.5122 2.0474 1.1084 550.808 305.492 993.117 68 1493 37.092 1.8959 0.7382 547.47 303.641 987.098 69 1479 42.0245 2.1029 0.8827 546.298 302.991 984.985 70 1466 18.3486 1.9662 0.7374 511.741 283.825 922.678 71 1453 40 2.2141 1.0378 556.686 308.752 1003.71 72 1442 16.129 1.9427 0.8341 552.612 306.493 996.369 73 1431 18.2927 2.0063 0.9856 559.77 310.463 1009.28 74 1421 19.8675 2.0442 0.7707 506.839 281.106 913.84 75 1411 24.8428 2.4278 1.0209 514.423 285.312 927.514 76 1402 19.281 2.3386 1.0033 497.393 275.867 896.808 77 1394 12.7726 2.372 0.9928 503.918 279.486 908.573 78 1386 15.7051 2.2705 0.9161 509.73 282.709 919.052 79 1379 8.53659 2.3457 0.9624 505.173 280.182 910.836 80 1372 15.016 2.3116 0.8032 490.422 272.001 884.24 81 1366 21.3855 2.4106 0.9347 502.574 278.74 906.15 82 1360 14.3312 2.2111 0.7737 489.813 271.663 883.142 83 1354 20.6687 2.384 0.9182 498.942 276.726 899.601 84 1349 11.315 2.4604 0.8583 495.313 274.713 893.058 85 1343 18.8406 2.3777 0.9369 494.39 274.201 891.394 86 1339 9.09091 2.2028 0.8815 514.897 285.575 928.368 87 1334 17.3295 2.3692 0.8885 503.651 279.338 908.092 88 1329 26.1261 2.2301 0.78 465.768 258.327 839.788 89 1325 33.121 2.3787 0.8343 487.214 270.221 878.455 90 1321 35.5623 2.3257 0.684 447.579 248.239 806.993 91 1316 37.6471 2.2888 0.8147 498.77 276.631 899.291 92 1312 46.5625 2.2558 0.7514 448.146 248.553 808.015 93 1308 39.1566 2.1191 0.8444 581.474 322.5 1048.41 94 1303 41.9786 1.9992 0.7434 515.288 285.792 929.073 95 1299 37.9205 1.9211 0.8709 560.815 311.042 1011.16 96 1294 39.314 1.9707 0.6931 531.459 294.761 958.23 97 1290 53.681 1.9925 0.8668 549.971 305.028 991.607 98 1285 38.5246 1.7148 0.8356 545.708 302.664 983.921 99 1280 47.2561 1.9445 1.1071 685.551 380.224 1236.06 100 1275 41.791 2.067 1.1133 553.924 307.22 998.735 101 1271 29.2899 2.2791 1.2091 565.171 313.458 1019.01 102 1266 21.8462 2.3746 0.9299 513.949 285.049 926.659 103 1261 35.0076 2.474 0.9944 522.396 289.734 941.889 104 1256 24.2515 2.5691 0.9202 506.641 280.996 913.483 105 1251 9.72644 2.4956 0.9139 531.3 294.673 957.943 106 1246 10.1266 2.5413 0.9212 487.944 270.626 879.772 107 1242 9.24855 2.5956 1.0314 490.004 271.769 883.486 108 1237 7.60234 2.5959 0.8611 491.983 272.866 887.054 109 1232 7.16418 2.5009 1.0781 510.905 283.361 921.171 110 1227 9.31677 2.7193 0.7899 472.857 262.259 852.57 111 1222 10.119 2.5943 0.9924 544.59 302.044 981.905 112 1217 14.0244 2.5839 0.9112 533.261 295.76 961.479 113 1212 8.86263 2.5368 1.0873 563.352 312.45 1015.73 114 1207 9.375 2.456 1.2489 637.25 353.435 1148.97 115 1202 5.88235 2.4256 1.2339 800.553 444.007 1443.41 116 1198 6.45161 2.3881 1.113 854.89 474.144 1541.38 117 1193 10.4116 2.4465 1.2654 647.858 359.319 1168.1 118 1188 12.5326 2.283 1.1038 503.06 279.01 907.026 119 1183 19.6375 2.2643 1.0096 561.125 311.214 1011.72 120 1178 23.8372 2.5409 1.094 510.658 283.224 920.725 121 1173 18.2353 2.5295 1.2142 1122.72 622.689 2024.28 122 1168 17.0732 2.1926 1.5584 2266.68 1257.16 4086.86 123 1163 15.7051 2.3322 1.392 1776.36 985.215 3202.81 124 1158 16.9972 2.3524 1.2904 832.07 461.487 1500.24 125 1153 19.0625 1.9684 1.5256 834.104 462.616 1503.9 126 1149 16.8605 1.8197 1.5479 899.746 499.022 1622.26 127 1144 19.2547 1.9336 1.4356 1136.74 630.465 2049.56